One strategy is transfer learning [1]}, whereby the model is first trained on data from other domains/tasks and then fine-tuned for the intended task. Transfer learning has been reported to improve the performance of CNN-based models on many medical image segmentation tasks [2]}, [3]}. A limitation of transfer learning is that most of the large public image datasets include only 2D images, whereas most medical images are 3D.
